Data Real-Time Acquisition Method and System Based on the Internet of Things

Abstract

The present invention relates to the field of data processing, and particularly to a method and system for real-time data acquisition based on the Internet of Things. The method includes: establishing corresponding processes for the data collected by multiple sensors, where the processes include process arrival times and service times; obtaining a process sequence based on the process arrival times and service times, and calculating process selection parameters according to the process sequence and service times; reordering the process sequence to obtain the number of times a process is cut in line, and calculating the concession degree of the process according to the process selection parameter and the number of times cut in line of the process; calculating the source service parameter of the process according to the concession degree of the process, and further calculating the service level parameter of the process, and further adjusting the sorting of the process sequence according to the service level parameter. The source service parameter represents the overall degree of being cut in line of the sensor corresponding to the process, and the service level parameter represents the degree of priority service of the process. The present invention effectively solves the problem of low efficiency in data acquisition and processing in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of data processing. More specifically, the present invention relates to a method and system for real-time data acquisition based on the Internet of Things. Background Art

[0002] The Internet of Things is an important direction for the development of modern technology, and real-time data acquisition is one of its core functions. Through various sensors and intelligent devices, the Internet of Things can collect real-time data in different environments, including temperature, humidity, pressure, light, location information, etc. The sensors are connected to the cloud platform through a wireless network, enabling the data to be transmitted and processed immediately. This real-time data acquisition not only improves the timeliness of the data but also enhances the accuracy and integrity of the information, providing a solid foundation for subsequent analysis.

[0003] In industrial production, the role of the real-time data acquisition system is particularly significant. By deploying sensors at all links of the production line, enterprises can monitor the equipment status and production process in real time, quickly identify potential faults, and reduce downtime. This predictive maintenance method not only improves production efficiency but also reduces operating costs. In addition, the real-time nature of the data enables enterprises to quickly respond to changes in market demand, optimize production plans and inventory management, and enhance overall operational flexibility.

[0004] Currently, a patent document with the publication number "CN109033387B" and the name "An Internet of Things search system, method, and storage medium for integrating multi-source data" discloses a method that can converge data generated by heterogeneous Internet of Things devices, industry data from multiple sources, and Internet data onto a platform to provide data support for data fusion analysis. In this method, the sorting of data involves sorting according to time.

[0005] The above method usually cannot sort data acquisition well, thus affecting the efficiency of data acquisition and processing. Summary of the Invention

[0006] To solve the problem of low efficiency in data acquisition and processing in the prior art, the present invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a method for real-time data acquisition based on the Internet of Things, including:

[0008] Establish corresponding processes for the data collected by multiple sensors. The process includes the arrival time and service time of the process; obtain the process sequence according to the arrival time and service time of the process, and calculate the process selection parameter according to the process sequence and the service time , where represents the process selection parameter of process i, denotes the sequence number of process i in the process sequence, denotes the service time of process i; re - sort the process sequence to obtain the number of times process i is cut in line, and calculate the concession degree of the process according to the process selection parameter and the number of times cut in line of the process , where denotes the concession degree of process i, denotes the process selection parameter of process i, denotes the number of times process i is cut in line, denotes the number of times the t - th process cutting in line of process i is cut in line, denotes the number of processes cutting in line of process i; according to the concession degree of the process calculate the source service parameter of the process, and then calculate the service level parameter of the process. Further adjust the sorting of the process sequence according to the service level parameter. The source service parameter is used to characterize the overall degree of being cut in line of the sensor corresponding to the process, and the service level parameter is used to characterize the priority service degree of the process.

[0009] By dynamically adjusting the process sequence and priority, the delay of data processing can be reduced. According to the concession degree and service level parameter of the process, resources can be allocated more reasonably to ensure that high - priority processes can obtain services in time. By calculating the number of times cut in line and the concession degree, it can effectively prevent some processes from not being processed for a long time and reduce the risk of process starvation. The source service parameter provides a reference for evaluating the overall performance of the sensor, which helps to optimize and improve the processes from this sensor, and finally solves the problem of low efficiency of data acquisition and processing in the current technology.

[0010] Preferably, the re - sorting of the process sequence includes: re - sorting the process sequence using the shortest job first algorithm.

[0011] The shortest job first algorithm can significantly reduce the average waiting time of processes by giving priority to short jobs, improve the overall efficiency. Processing short jobs first enables more processes to complete quickly, makes better use of system resources, and avoids processes that occupy resources for a long time from affecting the entire system.

[0012] Preferably, the data collected by the multiple sensors includes: at least two sensors collect data at a fixed period.

[0013] Through multiple sensors, more comprehensive and rich information can be obtained. Collecting data at a fixed period ensures the timeliness of data update.

[0014] Preferably, calculate the source service parameter of the process according to the concession degree of the process , including:

[0015] , where represents the source service parameter of sensor j, represents the degree of concession of the l-th process in sensor j, represents the degree of concession of the l-th process in sensor k, n represents the number of processes of sensor j, m represents the number of sensors, and max[] represents calculating the maximum value among all elements in the brackets [].

[0016] The comparison of the degree of concession of the processes of sensor j and other sensors is used to evaluate that the processes of sensor j do not have excessive service time or queue-jumping compared with the processes of other sensors, so as to judge whether the processes of sensor j need to be processed preferentially. Considering the situation of the entire sensor can avoid the overall delay of the processes of a certain sensor and also avoid the situation that the data of this sensor has been delayed and waiting, which may cause the system to get stuck.

[0017] Preferably, the service level parameter of the calculation process includes: , where represents the service level parameter of process i, and the process i comes from sensor j, represents the degree of concession of process i, represents the degree of concession of the l-th process in sensor j, represents the source service parameter of sensor j corresponding to process i, and max[] represents calculating the maximum value among all elements in the brackets [].

[0018] Calculating the service level parameter by comprehensively considering the specific process and the situation of the sensor corresponding to the process can more reasonably and comprehensively judge the preferential service degree of the process, so as to perform a more reasonable sorting on it.

[0019] Preferably, the further adjustment of the sorting of the process sequence according to the service level parameter includes: arranging the processes in the process sequence in descending order of the service level parameter.

[0020] Preferably, after the at least two sensors collect data according to a fixed period, it includes: performing missing data completion and abnormal correction processing on the collected data.

[0021] In a second aspect, the present invention also provides a data real-time acquisition system based on the Internet of Things, including: a memory and a processor, where a computer program is stored on the memory, and the processor executes the computer program to implement the above-mentioned data real-time acquisition method based on the Internet of Things.

[0022] The beneficial effects of the present invention are as follows: By dynamically adjusting the process sequence and priority, the latency of data processing can be reduced. According to the degree of concession of the process and the service level parameter, resources can be allocated more reasonably to ensure that high-priority processes can obtain services in a timely manner. By calculating the number of cut-in times and the degree of concession, it is possible to effectively prevent certain processes from not being processed for a long time and reduce the risk of process starvation. The source service parameter provides a reference for evaluating the overall performance of the sensor, which helps to optimize and improve the processes from this sensor, and ultimately solves the problem of low efficiency of data acquisition and processing in the current technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of example and not limitation, and like or corresponding reference numerals indicate like or corresponding parts, wherein:

[0024] Figure 1 is a flowchart of a method for real-time data acquisition based on the Internet of Things provided by an embodiment of the present invention;

[0025] Figure 2 is a block diagram of a system for real-time data acquisition based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0028] Figure 1 is a flowchart of a method for real-time data acquisition based on the Internet of Things according to an embodiment of the present invention, including the following steps:

[0029] S101. Establish corresponding processes for the data collected by multiple sensors.

[0030] In some embodiments, the receiving the data collected by the sensor includes: at least two sensors collecting data at a fixed period. For example, temperature, current, and voltage sensors collect data once every 5 seconds at a fixed period. After the at least two sensors collect data at a fixed period, it further includes: performing missing data filling and abnormal correction processing on the collected data. The missing data filling and abnormal correction processing of the collected data belong to well-known technologies and will not be elaborated herein.

[0031] An Internet of Things system usually consists of one or more servers and multiple sensors. The server is responsible for data processing, storage, and analysis, while the sensors are responsible for collecting data on the environment or devices. This architecture allows different sensors to send data to the server, which then integrates and analyzes this data to ultimately achieve functions such as monitoring, control, and automation. When receiving sensor data, the server usually needs to establish a process first. The specific process is as follows: The sensor sends a connection request to the server. After the server accepts the connection request, it establishes a process to receive the data collected by the sensor. The process includes the arrival time and service time of the process. The arrival time refers to the time when the process enters the ready queue, and the service time refers to the time required to process the process.

[0032] S102. Obtain the process sequence based on the arrival time and service time of the process, and calculate the process selection parameter according to the process sequence and service time.

[0033] The process selection parameter , where represents the serial number of process i in the process sequence, represents the service time of process i.

[0034] The obtaining of the process sequence based on the arrival time and service time of the process is specifically as follows: First, sort according to the arrival time of the process. The process with an earlier arrival time is sorted earlier. If multiple processes arrive at the same time, then sort according to the service time, and the process with a shorter service time is sorted earlier.

[0035] The process selection parameter represents the degree of preferential selection of process i in the current process sequence. The larger this value, the easier it is for process i to be selected for execution. It is related to the service time of the process and its serial number in the process sequence. For example, currently, there are two processes a and b in the process sequence, with serial numbers 3 and 5 respectively, and service times of 1 second and 3 seconds respectively. Then , , so process a is more likely to be selected for execution.

[0036] S103. Re - sort the process sequence to obtain the number of times process i is cut in line, and calculate the concession degree of the process according to the process selection parameter and the number of times the process is cut in line.

[0037] The concession degree of the process , where represents the process selection parameter of process i, represents the number of times process i is cut in line, represents the number of times the t - th process that cuts in line process i is cut in line, represents the number of processes that cut in line process i.

[0038] In some embodiments, reordering the process sequence includes: reordering the process sequence using the shortest job first algorithm. Since reordering may cause processes to jump the queue, the number of times each process is jumped can be obtained. For example, sorting processes according to the arrival time gives four processes with process numbers 1 / 2 / 3 / 4, arrival times of 0 second, 1 second, 2 seconds, and 3 seconds respectively, and service times of 8 seconds, 4 seconds, 2 seconds, and 3 seconds respectively. After reordering the process sequence using the shortest job first algorithm, starting from the current time 0, process 1 is executed. During the execution of process 1, processes 2, 3, and 4 have all arrived. At this time, the service time of process 3 is the shortest, so process 3 is executed first, then processes 4 and 2. Process 2 is jumped 2 times, and processes 3 and 4 are not jumped. The process selection parameter of process 2 , degree of concession In the calculation formula, since process 2 is jumped 2 times, so v = 2. The first process that jumps process 2 is process 3, and the first process that jumps process 2 is process 4, is the sum of the number of times process 3 and process 4 that jump process 2 are jumped, so .

[0039] S104. Calculate the source service parameter of the process according to the degree of concession of the process, and then calculate the service level parameter of the process. Further adjust the sorting of the process sequence according to the service level parameter. The source service parameter is used to characterize the overall degree of being jumped of the sensor corresponding to the process, and the service level parameter is used to characterize the priority service degree of the process.

[0040] In some embodiments, calculating the source service parameter of the process according to the degree of concession of the process , includes: , where represents the source service parameter of sensor j, represents the degree of concession of the l-th process in sensor j, represents the degree of concession of the l-th process in sensor k, n represents the number of processes of sensor j, m represents the number of sensors, and max[] represents calculating the maximum value of all elements in the brackets []. The larger the value, the more frequently the processes corresponding to this sensor are jumped relative to the processes corresponding to other sensors, and the processes of this sensor are often delayed. Therefore, it is necessary to improve the priority service level of the processes corresponding to this sensor.

[0041] For example, an Internet of Things system has three sensors A, B, and C that collect data as sensors. The data collection period of the three sensors is 5 seconds. Currently, there are three cycles of data corresponding processes waiting to be processed, that is, there are nine processes A1, B1, C1, A2, B2, C2, A3, B3, and C3 waiting to be processed. If we want to calculate the source service parameter of sensor A , in the formula , is A, m represents the number of sensors, that is, m = 3, and n represents the number of processes of sensor A, that is, n = 3. Substituting the concession degree of each process can calculate the source service parameter of sensor A . When , it means that the process of sensor A does not have an excessive concession degree compared with the processes of other sensors, indicating that there is no excessive service time or queue-jumping situation for the process of sensor A compared with the processes of other sensors. Therefore, there is no need to increase the priority service level of the process corresponding to this sensor. Otherwise, it is necessary to further improve the priority service level of the process corresponding to this sensor.

[0042] In some embodiments, the calculation of the service level parameter of the process , includes: , where represents the service level parameter of process i, The larger the value, the more process i should be given priority service or processing. The process i comes from sensor j, represents the concession degree of process i, represents the concession degree of the l-th process in sensor j, represents the source service parameter of sensor j corresponding to process i, and max[] represents calculating the maximum value of all elements in the brackets [].

[0043] When ≤1, it means that the concession degree of process i is less than that of most processes of sensor , indicating that the queue-jumping situation of process i is less than that of most processes of sensor j. Therefore, process i does not need to further improve the priority service level of process i. Otherwise, when > 1, the queue-jumping situation of process i may exceed that of most processes of sensor j. In order to prevent excessive waiting, it is necessary to further improve the priority service level of process i.

[0044] Combined with , the concession degree of process i and the source service parameter of its sensor j The calculated can more reasonably and accurately evaluate the priority service level of each process. Then, according to the service level parameter The process sequence is further adjusted and arranged in descending order of the service level parameter.

[0045] The above-mentioned data real-time acquisition method based on the Internet of Things provided by the embodiment of the present invention can reduce the delay of data processing by dynamically adjusting the process sequence and priority. According to the concession degree of the process and the service level parameter, resources can be allocated more reasonably to ensure that high-priority processes can obtain services in a timely manner. By calculating the cut-in times and the concession degree, it can effectively prevent some processes from not being processed for a long time and reduce the risk of process starvation. The source service parameter provides a reference for evaluating the overall performance of the sensor, which helps to optimize and improve the processes from the sensor, and finally solves the problem of low efficiency of data acquisition and processing in the current technology.

[0046] The present invention also provides a data real-time acquisition system based on the Internet of Things. As Figure 2 shown, the system includes a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, the data real-time acquisition method based on the Internet of Things described in the present invention is implemented.

[0047] The system also includes a communication bus, a communication interface and other components well known to those skilled in the art. Their settings and functions are known in the art, so they will not be described in detail here.

[0048] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device or device. For example, the computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory RRAM (Resistive Random Access Memory), dynamic random access memory DRAM (Dynamic Random Access Memory), static random access memory SRAM (Static Random-Access Memory), enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), high-bandwidth memory HBM (High-Bandwidth Memory), hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application program, module or both. Any such computer storage medium can be part of the device or accessible or connectable to the device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions that can be stored or otherwise held by such a computer-readable medium.

[0049] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three or more, etc., unless otherwise specifically defined.

[0050] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations and alternative ways without departing from the spirit and concept of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein may be adopted in the practice of the present invention.

Claims

1. A method for real-time data acquisition based on the Internet of Things, characterized in that, Including: Establish corresponding processes for the data collected by multiple sensors, where the processes include the arrival time and service time of the processes; Obtain the process sequence according to the arrival time and service time of the processes, and calculate the process selection parameter according to the process sequence and service time , where represents the process selection parameter of process i, represents the serial number of process i in the process sequence, represents the service time of process i; Reorder the process sequence to obtain the number of times the process is cut in line, and calculate the degree of concession of the process according to the process selection parameter and the number of times the process is cut in line , where represents the degree of concession of process i, represents the process selection parameter of process i, represents the number of times process i is cut in line, represents the number of times the t-th process of the cut-in process i is cut in line, represents the number of processes of the cut-in process i; According to the concession degree of the process Calculate the source service parameter of the process, and then calculate the service level parameter of the process. Further adjust the sorting of the process sequence according to the service level parameter. The source service parameter is used to characterize the overall degree of being cut in line of the sensor corresponding to the process, and the service level parameter is used to characterize the priority service degree of the process; Calculating the source service parameter of a process according to the concession degree of the process , including: , where represents the source service parameter of sensor j, represents the degree of concession of the l-th process in sensor j, represents the degree of concession of the l-th process in sensor k, n represents the number of processes of sensor j, m represents the number of sensors, and max[] represents calculating the maximum value among all elements in the brackets []. The service level parameter of the computing process , including: , where represents the service level parameter of process i, and the process i is from sensor j. represents the degree of concession of process i. represents the degree of concession of the l-th process in sensor j. represents the source service parameter of sensor j corresponding to process i, and max[] represents the maximum value among all elements in the brackets [].

2. The data real-time acquisition method based on the Internet of Things according to claim 1, wherein Said reordering the process sequence includes: Reordering the process sequence using the shortest job first algorithm.

3. The method for real-time data acquisition based on the Internet of Things according to claim 1, characterized in that The data collected by the multiple sensors includes: at least two sensors collecting data at fixed intervals.

4. The data real-time acquisition method based on the Internet of Things according to claim 1, wherein Said further adjusting the sorting of the process sequence according to the service level parameter includes: Arranging the processes in the process sequence in descending order according to the service level parameter.

5. The data real-time acquisition method based on the Internet of Things according to claim 3, characterized in that, After the at least two sensors collect data at fixed intervals, it includes: performing missing data completion and anomaly correction processing on the collected data.

6. A real-time data acquisition system based on the Internet of Things, characterized in that, Including: A memory and a processor, where a computer program is stored on the memory, and the processor executes the computer program to implement the real-time data collection method based on the Internet of Things according to any one of claims 1-5.

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